March 3rd, ’23

Overview

1 | background

2 | questions

3 | approaches

4 | methods

5 | results

6 | future

7 | conclusions

Team

Jane Ogilvie (ecological fieldwork, design)

Emily J. Woodworth (pollen morphology, and microscopy)

Sophie Taddeo (geo-spatial, statistics)

Paul CaraDonna (little bees/big picture(s))

Jeremie Fant (all things molecular and tied together)

i.e. an in-house production.

the world is big - 1.1

… really really big - 1.2

funding opportunties - 1.3

sample the planet - 1.4

  • Bullet 2
  • Bullet 3

Primary Roads

from and back to functional forms - 1.5

plant species in ecology - 1.6

  • mis-identification is very common
  • mis-identification can lead to nebulous understandings
  • mis-identification can lead to mis-management

Cirsium scariosum

insects species in ecology - 1.7

  • Macro Invertebrates
    • stream ecology bio indicators
      • mayflies, caddisflies, stoneflies
  • Coleoptera
    • soil contamination by metals

from organisms to interactions - 1.8

  • Bullet 2
  • Bullet 3

Solidago spathulata & Megachile wheeleri, by A. Litz

pollen records - 1.9

metabarcoding - 1.10

  • Barcoding
    • molecular identification of tissue from a single organism
  • Metabarcoding
    • molecular identification of organisms present in a mixed substrate

Five Astragali

barcodes - 1.11

  • Kingdom: Animal,
    • COI (Cytochrome c OxIdase)
    • holding it’s own in Fungi
  • Kingdoms: Fungi + Plant
    • ITS (Interal Transcribed Spacer)
    • holding it’s own in Fungi
  • Kingdom: Plant
    • ITS, rbcL, matK, trnH-psbA
    • not holding much of anything

new barcodes for plants? - 1.12

  • genomic
    • low cost
  • how to reference backbone system

angiosperms 353 - 1.13

2 - questions

a353 as barcodes? - 2.1

  • ‘universal’ markers for phylogenomics
  • usable in all flowering plant clades
  • first comprehensive genus level phylogeny of flowering plants

serverless genomics? - 2.2

time and species rich clades - 2.3

  • diverse clades provide challenges for identification
  • species often diverged in ecological traits
  • flowering time provides a filter for pollination studies

Rhododendron Hengduan Mtns., by Qin Li

are a353 semi-quantitative? - 2.4

  • identification good

  • interactions great

  • quantify importance of interactions awesome

are floral visitation and pollen records congruent? - 2.5

3 - approaches

Flora free data - 3.1

  • no longer any funding
  • very few hobbyists
  • how do we monitor ecological shifts under climate change?
    • geographic ranges
    • flowering time

  • back to the sheets!

FPNW 2nd

custom sequence databases - 3.2

  • reduce number of species present in database
    • reduce computational requirements
    • increase likelihood of relevant matches across loci
    • reduce false positives for semi-quantitative inference

identify pollen grains - 3.3

queen bee pollen loads - 3.4

4 - methods

  • field work
  • spatial
  • temporal
  • morphologic
  • laboratory
  • bioinformatic
  • post-classification

study system & field work - 4.1

pollen morphological identification 4.2

workflow

pollen reference library 4.2.1

  • ca. 110 species
  • 60 novels species added
  • shared
  • many more species to add to key!! (60 +, mostly un- sampled families)

pollen corbiculae loads 4.2.2

  • aliquot from same sample used for molecular
  • stained by fuchsin jelly with stirring
  • transects
  • rarefaction curves
    • richness
    • abundance

Corbiculae Sample

molecular barcoding 4.3

spatial analysis 4.3.1

plant species, distribution modelling 4.3.1.1.

  • develop a candidate species list for barcoding
  • download all herbarium records from a distance exceeding the study area
  • compare to known species at field site
  • logistic regression
  • bootstrapped samples of records

species distribution modelling - 4.3.1.2

sdm evaluations - 4.3.1.2

  • in pipeline, True skill statistics

temporal modelling - 4.3.2

  • reduce herbarium records to study domain
  • thin records to analogous ecoregions
  • trim start/end records
  • identify major phenological cues, subset records to similar areas

temporal modelling subset - 4.3.2.1

SPATIAL SUBSET PICTURE

temporal modelling distributions - 4.3.2.2

WEIBULL PICTURE ?

barcode references library - 4.4

genomics work - 4.4.1

  • Plant Reference Library
    • herbarium & silica dried
    • CTAB, some DNEasy
  • Pollen Extraction
    • ‘novel’ CTAB / SDS extraction
  • Both
    • clean up Cytiza, size selection SPRI
    • enzymatic fragmentation

plant genomic reference dna - 4.4.2

  • 38 species to sequencing
  • 13 species duplicate
  • 24 silica gel dried, 14 herbarium leaf tissue (RM, ID, IDS)

tissue from Rocky Mountain Herbarium

pollen genomics dna - 4.4.3

  • 54 Initial samples for extraction
  • 44 samples underwent all steps

hyb-seq

barcoding informatics - 4.4.4

IMAGE OF COMPUTER BUILDING

metabarcoding - 4.5

sequence database generation - 4.5.1

  • Kew Tree of Life ~ ### taxa
  • US ~ xx TAXA

database

sequence assignment - 4.5.2

semi-quantitative evidence 4.5.3

5

results

field work

  • 723 floral visitation observations (!)
  • 36 unique plant species involved
  • 64 corbiculae loads from Queens

sdm candidate species ????

  • downloaded some 112k records
  • mostly trees from forestry surveys
  • bootstrap re-sampled to reduce effects of collection ‘hotspots’
  • non-present taxa begin nearly immediately…
  • real occurrences taper off quickly

database

sdm evaluations - computational - 5.2

Logistic regression assessing accuracy of SDMs; witheld data
Metric Value Metric Value
Accuracy (Training) 83.75 F-Score 0.84
Accuracy (Test) 84.00 AUC 0.92
Recall 81.03 Concordance 0.92
True Neg. Rate 86.97 Discordance 0.08
Precision 88.04 Tied 0.00

sdm evaluations - 5.3.1

  • able to compare to a localized vascular plant checklist
    • not everything w/ vouchers…
    • not everything w/ vouchers…
  • able to remove nearly all species from the upper (alpine) & lower (sagesteppe) life zones
ml lm
ensembles 493 473
true + 362 286
true - 33 55
false + 64 41
false - 34 93

sdm evaluations - 5.3.2

  • We were interested in comparison to the Valleys.
  • Plot Level, 117 species total (109 eligible for modelling…)
    • ML: 105 (89.7% (96.3%))
    • LM: 102 (87.2% (93.5%))
  • Able to detect virtually all species recorded on plot

coarse phenological modelling - 5.4.1

  • strong agreement between first and peak flower periods with historic data
  • good agreement between last flower date
  • no agreement with duration! - species do not ‘line up’

flower dates

coarse phenological modelling - 5.4.2

  • similar results with weekly data across all field sites combined
  • tau values lower than over longer term data

flower dates

metabarcoding - 5.5

sequence database generation - 5.6

  • found existing data for 130 species on NCBI - SRA
  • novel sequence data for 25 species, varying number of loci
  • whole ‘ring’ to be completed within the year

Species in Sequence DB

sequence assignment - 5.7 - I

  • trimmomatic (discard short reads)

  • Kraken (many false positives)

  • Bracken (many many false positives)

  • Blast (least false positives)

  • both very fast alignment algorithms, worked well as filters to reduce the workload for BLAST

    • BLAST operates at roughly 1/1000 speed
    • more accurate algorithm

Three Initial Networks

sequence assignment - 5.7 - II

## [1] 99.9
Post classification of Sequences via Taxonomy and Ecology, top 15 most abundant reads
Condition No. Class. Prcnt. Class. Total Seqs Rank
A 143 21.0 32.0 Species
B 205 30.1 10.5 Species
C 5 0.7 0.4 Genus
G 29 4.3 7.8 Species
H 280 41.2 47.9 Genus
None met 18 2.6 1.4 Multiple

sequence assingment - 5.7 - III

  • conceptually similar to the automated process
  • utilized high resolution occurrence and phenology data
  • utilized morphological and molecular data
  • no linear operation or rule of precedence
  • classified all sequences to species

semi-quantitative evidence - 5.8

  • …
  • some relationship exists
  • requires further work

Counted grains

final floral feeding structure - 5.9

6

Discussion

conservation implications - 6.1 - I

  • a hidden plain text paragraph in the discussion.
  • collaboration with Ken Holsinger, Jedd Sondergard @ BLM Montrose

” Some foraging preferences of Bombus, both at this field site and across a great many localities globally emerge from this work, which reiterates the needs for land managers to maintain relatively high amounts of members of the Fabaceae, Boraginaceae, and Ranunculaceae, in Western North American montane landscapes (@goulson2005causes, @goulson2010bumblebees, @liang2021evolutionary, @bontvsutvsnaja2021bumble). Numerous historic, and some ongoing, land management practices reduce the ability of many landscapes to support stable populations of Bombus. Historic livestock grazing was often associated with the targeted removal of many species of plants which are known to have compounds toxic to cattle. In particular, the removal of locoweeds (Fabaceae: Astragalus L. & Oxytropis DC.) and larkspurs (Ranunculaceae: Delphinium) were common across public lands administered by the United States Forest Service (@ralphs1988herbicide, @aldous1919eradicating, @ralphs2003mechanism). Further actions, generally initiated by early settlers, involved the channelization and incising of streams, culling of beavers, and leaving cattle concentrated on higher order stream banks for significant periods of time, all processes which lower the water tables and reduced the extent of stream-associated [riverine] wetlands and the mesic meadows fringes which provide habitat for many species of tall Mertensia (Boraginaceae, e.g. M. ciliata Torr. G. Don.) widely distributed across Western North America, and to an extent Delphinium barbeyi and many species of native Trifolium L. (@dahl1990wetlands, @naiman1988alteration, @belsky1999survey, @cooke1976arroyos). Fire suppression further resulted in the succession of many Aspen (Populus tremuloides Michx.) groves to Conifer stands, decreasing the mosaic of age structured habitats in many landscapes, adversely effects habitat for tall Mertensia species and several species of Delphinium (@brewen202176, @keane2002cascading). Finally the effects of Nitrogen deposition, especially given the West’s rapidly growing population still pose adverse effects on the abundance of a variety of species of Fabaceae at Urban-Rural interfaces (see @stevens2018atmospheric, @fenn2003ecological). Current solutions to these issues, involve targeted burns, reintroduction of beavers and beaver habitat analogs, and the possibility of re-seeding a variety of ‘locoweeds’ and ‘larkspurs’ in areas now seldom used, or only used for early, grazing. The highly enthusiastic response of land managers, and homeowners, to plant Ascelpias L., using genetically appropriate materials, to improve Monarch Butterfly (Danaus plexippus L.) habitat provides an effective framework for the latter (@oberhauser2015monarchs, @basey2015producing). ”

conservation implications - 6.1 - II

  • historic vegetation treatment removals
    • Delphinium, Astragalus & Oxytropis
  • altered fire cycle
    • Delphinium, Mertensia
  • stream channelization / wetland removal
    • Mertensia
  • seed species, where missing
  • allow return of historic fire cycle, when appropriate
  • reintroduce beavers, or if unavailable than analogs

7

future

metabarcoding; computational approaches - 7.2

  • qualitative:
    • search for variable loci
    • flanking regions and pop gen

  • quantitative:
    • read re-assignment based on phylogentic distance
    • read re-assignment in bayesian framework

metabarcoding; new data sets? - 7.1

  • (more) West Slope
  • artificial mixtures
    • leaf tissue (counted cells)
      • easier to collect
    • pollen loads (counted grains)
      • more popular
  • Gunnison Sage-Grouse scat
    • BLM habitat assessment data
    • opportunistic collections

bombus; trends in perennial bunchgrasses - 7.3

  • notable declines in bunch grasses over the last 40 years
  • especially pronounced in montane areas
  • model conversion of meadows/Aspen stands to conifer forest
  • use existing data to model reductions in grass cover
  • view historic grazing renewals to monitor (loosely) reduction in grass diversity
  • forked from Ken Holsinger

8

conclusions

promising

acknowledgements

two super fantastic technicians for the field seasons I worked during school, made my life SO easy
find them funding and recruit them, and then give me a finders fee.

  • Dani Yashinowitz B.S. (Yellowstone National Park, botanist & crew lead, Whitebark Pine Surveys (!!!))
  • Hannah Lovell B.S. (Telluride Mountain Resort, and in search of work)

acknowledgments

Employment: Yingying Xie, Josh Scholl, Sam Isham, Kelly McMillen, Kay Hajek, Linda Vance, Cassandra Owen, Ken Holsinger

Project: Nyree Zerega, Pat Herendeen, Hilary Noble, Zoe Diaz-Martinez, Angela McDonnell, Elena Loke, Ian Breckheimer, Ben Legler, Ernie Nelson, Charles (Rick) Williams, D. Knoke, L. Brummer, J. Boyd, C. Davidson, I. Gilman, M. Kirkpatrick, S. McCauley, J. Smith, K. Taylor, & C. Williams. David Giblin, Mare Nazaire, Sarah Burnett, Lauren Price, T.C.H. Cole, Eliot Gardner.